"""Pytorch Densenet implementation w/ tweaks
This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
fixed kwargs passthrough and addition of dynamic global avg/max pool.
"""
import re
from collections import OrderedDict
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from torch.jit.annotations import List

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import BatchNormAct2d, create_norm_act, BlurPool2d, create_classifier
from .registry import register_model

__all__ = ["DenseNet"]


def _cfg(url=""):
    return {
        "url": url,
        "num_classes": 1000,
        "input_size": (3, 224, 224),
        "pool_size": (7, 7),
        "crop_pct": 0.875,
        "interpolation": "bicubic",
        "mean": IMAGENET_DEFAULT_MEAN,
        "std": IMAGENET_DEFAULT_STD,
        "first_conv": "features.conv0",
        "classifier": "classifier",
    }


default_cfgs = {
    "densenet121": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenet121_ra-50efcf5c.pth"
    ),
    "densenet121d": _cfg(url=""),
    "densenetblur121d": _cfg(
        url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenetblur121d_ra-100dcfbc.pth"
    ),
    "densenet169": _cfg(
        url="https://download.pytorch.org/models/densenet169-b2777c0a.pth"
    ),
    "densenet201": _cfg(
        url="https://download.pytorch.org/models/densenet201-c1103571.pth"
    ),
    "densenet161": _cfg(
        url="https://download.pytorch.org/models/densenet161-8d451a50.pth"
    ),
    "densenet264": _cfg(url=""),
    "densenet264d_iabn": _cfg(url=""),
    "tv_densenet121": _cfg(
        url="https://download.pytorch.org/models/densenet121-a639ec97.pth"
    ),
}


class DenseLayer(nn.Module):
    def __init__(
        self,
        num_input_features,
        growth_rate,
        bn_size,
        norm_layer=BatchNormAct2d,
        drop_rate=0.0,
        memory_efficient=False,
    ):
        super(DenseLayer, self).__init__()
        self.add_module("norm1", norm_layer(num_input_features)),
        self.add_module(
            "conv1",
            nn.Conv2d(
                num_input_features,
                bn_size * growth_rate,
                kernel_size=1,
                stride=1,
                bias=False,
            ),
        ),
        self.add_module("norm2", norm_layer(bn_size * growth_rate)),
        self.add_module(
            "conv2",
            nn.Conv2d(
                bn_size * growth_rate,
                growth_rate,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False,
            ),
        ),
        self.drop_rate = float(drop_rate)
        self.memory_efficient = memory_efficient

    def bottleneck_fn(self, xs):
        # type: (List[torch.Tensor]) -> torch.Tensor
        concated_features = torch.cat(xs, 1)
        bottleneck_output = self.conv1(self.norm1(concated_features))  # noqa: T484
        return bottleneck_output

    # todo: rewrite when torchscript supports any
    def any_requires_grad(self, x):
        # type: (List[torch.Tensor]) -> bool
        for tensor in x:
            if tensor.requires_grad:
                return True
        return False

    @torch.jit.unused  # noqa: T484
    def call_checkpoint_bottleneck(self, x):
        # type: (List[torch.Tensor]) -> torch.Tensor
        def closure(*xs):
            return self.bottleneck_fn(xs)

        return cp.checkpoint(closure, *x)

    @torch.jit._overload_method  # noqa: F811
    def forward(self, x):
        # type: (List[torch.Tensor]) -> (torch.Tensor)
        pass

    @torch.jit._overload_method  # noqa: F811
    def forward(self, x):
        # type: (torch.Tensor) -> (torch.Tensor)
        pass

    # torchscript does not yet support *args, so we overload method
    # allowing it to take either a List[Tensor] or single Tensor
    def forward(self, x):  # noqa: F811
        if isinstance(x, torch.Tensor):
            prev_features = [x]
        else:
            prev_features = x

        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("Memory Efficient not supported in JIT")
            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bottleneck_fn(prev_features)

        new_features = self.conv2(self.norm2(bottleneck_output))
        if self.drop_rate > 0:
            new_features = F.dropout(
                new_features, p=self.drop_rate, training=self.training
            )
        return new_features


class DenseBlock(nn.ModuleDict):
    _version = 2

    def __init__(
        self,
        num_layers,
        num_input_features,
        bn_size,
        growth_rate,
        norm_layer=nn.ReLU,
        drop_rate=0.0,
        memory_efficient=False,
    ):
        super(DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = DenseLayer(
                num_input_features + i * growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size,
                norm_layer=norm_layer,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
            self.add_module("denselayer%d" % (i + 1), layer)

    def forward(self, init_features):
        features = [init_features]
        for name, layer in self.items():
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)


class DenseTransition(nn.Sequential):
    def __init__(
        self,
        num_input_features,
        num_output_features,
        norm_layer=nn.BatchNorm2d,
        aa_layer=None,
    ):
        super(DenseTransition, self).__init__()
        self.add_module("norm", norm_layer(num_input_features))
        self.add_module(
            "conv",
            nn.Conv2d(
                num_input_features,
                num_output_features,
                kernel_size=1,
                stride=1,
                bias=False,
            ),
        )
        if aa_layer is not None:
            self.add_module("pool", aa_layer(num_output_features, stride=2))
        else:
            self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """

    def __init__(
        self,
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        bn_size=4,
        stem_type="",
        num_classes=1000,
        in_chans=3,
        global_pool="avg",
        norm_layer=BatchNormAct2d,
        aa_layer=None,
        drop_rate=0,
        memory_efficient=False,
        aa_stem_only=True,
    ):
        self.num_classes = num_classes
        self.drop_rate = drop_rate
        super(DenseNet, self).__init__()

        # Stem
        deep_stem = "deep" in stem_type  # 3x3 deep stem
        num_init_features = growth_rate * 2
        if aa_layer is None:
            stem_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        else:
            stem_pool = nn.Sequential(
                *[
                    nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
                    aa_layer(channels=num_init_features, stride=2),
                ]
            )
        if deep_stem:
            stem_chs_1 = stem_chs_2 = growth_rate
            if "tiered" in stem_type:
                stem_chs_1 = 3 * (growth_rate // 4)
                stem_chs_2 = (
                    num_init_features
                    if "narrow" in stem_type
                    else 6 * (growth_rate // 4)
                )
            self.features = nn.Sequential(
                OrderedDict(
                    [
                        (
                            "conv0",
                            nn.Conv2d(
                                in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False
                            ),
                        ),
                        ("norm0", norm_layer(stem_chs_1)),
                        (
                            "conv1",
                            nn.Conv2d(
                                stem_chs_1,
                                stem_chs_2,
                                3,
                                stride=1,
                                padding=1,
                                bias=False,
                            ),
                        ),
                        ("norm1", norm_layer(stem_chs_2)),
                        (
                            "conv2",
                            nn.Conv2d(
                                stem_chs_2,
                                num_init_features,
                                3,
                                stride=1,
                                padding=1,
                                bias=False,
                            ),
                        ),
                        ("norm2", norm_layer(num_init_features)),
                        ("pool0", stem_pool),
                    ]
                )
            )
        else:
            self.features = nn.Sequential(
                OrderedDict(
                    [
                        (
                            "conv0",
                            nn.Conv2d(
                                in_chans,
                                num_init_features,
                                kernel_size=7,
                                stride=2,
                                padding=3,
                                bias=False,
                            ),
                        ),
                        ("norm0", norm_layer(num_init_features)),
                        ("pool0", stem_pool),
                    ]
                )
            )
        self.feature_info = [
            dict(
                num_chs=num_init_features,
                reduction=2,
                module=f"features.norm{2 if deep_stem else 0}",
            )
        ]
        current_stride = 4

        # DenseBlocks
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                norm_layer=norm_layer,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
            module_name = f"denseblock{(i + 1)}"
            self.features.add_module(module_name, block)
            num_features = num_features + num_layers * growth_rate
            transition_aa_layer = None if aa_stem_only else aa_layer
            if i != len(block_config) - 1:
                self.feature_info += [
                    dict(
                        num_chs=num_features,
                        reduction=current_stride,
                        module="features." + module_name,
                    )
                ]
                current_stride *= 2
                trans = DenseTransition(
                    num_input_features=num_features,
                    num_output_features=num_features // 2,
                    norm_layer=norm_layer,
                    aa_layer=transition_aa_layer,
                )
                self.features.add_module(f"transition{i + 1}", trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module("norm5", norm_layer(num_features))

        self.feature_info += [
            dict(
                num_chs=num_features, reduction=current_stride, module="features.norm5"
            )
        ]
        self.num_features = num_features

        # Linear layer
        self.global_pool, self.classifier = create_classifier(
            self.num_features, self.num_classes, pool_type=global_pool
        )

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)

    def get_classifier(self):
        return self.classifier

    def reset_classifier(self, num_classes, global_pool="avg"):
        self.num_classes = num_classes
        self.global_pool, self.classifier = create_classifier(
            self.num_features, self.num_classes, pool_type=global_pool
        )

    def forward_features(self, x):
        return self.features(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.global_pool(x)
        # both classifier and block drop?
        # if self.drop_rate > 0.:
        #     x = F.dropout(x, p=self.drop_rate, training=self.training)
        x = self.classifier(x)
        return x


def _filter_torchvision_pretrained(state_dict):
    pattern = re.compile(
        r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
    )

    for key in list(state_dict.keys()):
        res = pattern.match(key)
        if res:
            new_key = res.group(1) + res.group(2)
            state_dict[new_key] = state_dict[key]
            del state_dict[key]
    return state_dict


def _create_densenet(variant, growth_rate, block_config, pretrained, **kwargs):
    kwargs["growth_rate"] = growth_rate
    kwargs["block_config"] = block_config
    return build_model_with_cfg(
        DenseNet,
        variant,
        pretrained,
        default_cfg=default_cfgs[variant],
        feature_cfg=dict(flatten_sequential=True),
        pretrained_filter_fn=_filter_torchvision_pretrained,
        **kwargs,
    )


@register_model
def densenet121(pretrained=False, **kwargs):
    r"""Densenet-121 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "densenet121",
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        pretrained=pretrained,
        **kwargs,
    )
    return model


@register_model
def densenetblur121d(pretrained=False, **kwargs):
    r"""Densenet-121 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "densenetblur121d",
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        pretrained=pretrained,
        stem_type="deep",
        aa_layer=BlurPool2d,
        **kwargs,
    )
    return model


@register_model
def densenet121d(pretrained=False, **kwargs):
    r"""Densenet-121 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "densenet121d",
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        stem_type="deep",
        pretrained=pretrained,
        **kwargs,
    )
    return model


@register_model
def densenet169(pretrained=False, **kwargs):
    r"""Densenet-169 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "densenet169",
        growth_rate=32,
        block_config=(6, 12, 32, 32),
        pretrained=pretrained,
        **kwargs,
    )
    return model


@register_model
def densenet201(pretrained=False, **kwargs):
    r"""Densenet-201 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "densenet201",
        growth_rate=32,
        block_config=(6, 12, 48, 32),
        pretrained=pretrained,
        **kwargs,
    )
    return model


@register_model
def densenet161(pretrained=False, **kwargs):
    r"""Densenet-161 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "densenet161",
        growth_rate=48,
        block_config=(6, 12, 36, 24),
        pretrained=pretrained,
        **kwargs,
    )
    return model


@register_model
def densenet264(pretrained=False, **kwargs):
    r"""Densenet-264 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "densenet264",
        growth_rate=48,
        block_config=(6, 12, 64, 48),
        pretrained=pretrained,
        **kwargs,
    )
    return model


@register_model
def densenet264d_iabn(pretrained=False, **kwargs):
    r"""Densenet-264 model with deep stem and Inplace-ABN"""

    def norm_act_fn(num_features, **kwargs):
        return create_norm_act("iabn", num_features, **kwargs)

    model = _create_densenet(
        "densenet264d_iabn",
        growth_rate=48,
        block_config=(6, 12, 64, 48),
        stem_type="deep",
        norm_layer=norm_act_fn,
        pretrained=pretrained,
        **kwargs,
    )
    return model


@register_model
def tv_densenet121(pretrained=False, **kwargs):
    r"""Densenet-121 model with original Torchvision weights, from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = _create_densenet(
        "tv_densenet121",
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        pretrained=pretrained,
        **kwargs,
    )
    return model
